mSimCC (version 0.0.3)

plotMortality: Calculates and plots the cervical cancer mortality.

Description

Calculates and plots the cervical cancer mortality for one or several prevention strategies.

Usage

plotMortality(..., current=NULL, labels=NULL)

Value

Returns a list with cervical cancer mortality for each age group.

Arguments

...

one or several microsimulated cohort corresponding to one or several microsimulated cohorts.

current

real cervical cancer mortality in the population of interest.

labels

labels to be used in the plot.

Author

David Moriña (Universitat de Barcelona), Pedro Puig (Universitat Autònoma de Barcelona) and Mireia Diaz (Institut Català d'Oncologia)

References

Georgalis L, de Sanjosé S, Esnaola M, Bosch F X, Diaz M. Present and future of cervical cancer prevention in Spain: a cost-effectiveness analysis. European Journal of Cancer Prevention 2016;25(5):430-439.

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

See Also

mSimCC-package, microsim, costs, le, bCohort, plotCIN2Incidence, plotCIN1Incidence, plotCIN3Incidence, plotMortality, plotPrevalence, qalys, yls

Examples

data(probs)
nsim       <- 3
p.men      <- 0
size       <- 20
min.age    <- 10
max.age    <- 84

#### Natural history
hn <- microsim(seed=1234, nsim, probs, abs_states=c(10, 11), sympt_states=c(5, 6, 7, 8), 
               prob_sympt=c(0.11, 0.23, 0.66, 0.9), 
                size, p.men, min.age, max.age, 
                utilityCoefs = c(1, 1, 0.987, 0.87, 0.87, 0.76, 0.67, 0.67, 0.67, 0.938, 0, 0),
                costCoefs.md = c(0, 0, 254.1, 1495.9, 1495.9, 5546.8, 12426.4, 23123.4, 
                                 34016.6, 0, 0, 0),
                costCoefs.nmd = c(0, 0, 81.4, 194.1, 194.1, 219.1, 219.1, 219.1, 219.1, 0, 0, 0),
                costCoefs.i = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), disc=3, 
                treatProbs=c(0,0,1,1,1,0.9894,0.9422,0.8262,0.5507,0,0,0),
                nCores=1) ### individual level
hn_c <- bCohort(hn)
plotMortality(hn_c) ### Aggregated level